Drug Discovery Is Inefficient And Expensive
96% of all drugs taken into clinical trials fail, costing us $2.6 billion per successful drug and resulting in our extremely high cost of healthcare. This is unacceptable.
The most common reason for failure is the inadequate validation of drug targets, because our understanding of science has not been able to account for the massive complexity in human biology - until now.
Complex problems require computational solutions. We use cutting-edge systems biology and AI to reverse-engineer biology, and de-risk drug development programs at the earliest stages of drug development.
We Understand From First Principles
Science is hard. We don't make it harder by using inaccurate information.
Our team has developed a novel state-of-the-art drug discovery platform, using only primary biological data, that provides a strong predictive framework to computationally model causal influences over disease pathways.
We do this by starting with first curating high-quality gene expression, epigenetic and proteomic data in target tissues, and computationally map disease pathways.
Dr. Chee Yang Chen, MBBS
Dr. Liisi Laaniste-Blevins, PhD
Dr. Mala Mawkin, MBBS
Asst Prof. Prashant Srivastava, PhD
VP Computational Biology
Asst Prof. Sohag Saleh, PhD
We Reverse Genomic Maps To Reverse Disease
We create maps and find the shortest path to our destination.
Using insights from our proprietary network maps, we systematically uncover drug targets that induce an opposite signature to disease, restoring cells directly from disease states back to health.
Our drug targets are then robustly tested using AI for accuracy to significantly reduce drug development costs, and taken by our expert team into development, to much higher rates of success.